Structure Discovery in Mixed Order Hyper Networks

Background  Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be used for regression, classification or as content addressable memories and have been shown to be useful as fitness function models in constraint satisfaction tasks. They are fast to train and, when their structure is fixed, do not suffer from local minima in the cost function during training. However, their main drawback is that the correct structure (which neurons to connect with weights) must be discovered from data and an exhaustive search is not possible for networks of over around 30 inputs.  Results  This paper presents an algorithm designed to discover a set of weights that satisfy the joint constraints of low training error and a parsimonious model. The combined structure discovery and weight learning process was found to be faster, more accurate and have less variance than training an MLP.  Conclusions  There are a number of advantages to using higher order weights rather than hidden units in a neural network but discovering the correct structure for those weights can be challenging. With the method proposed in this paper, the use of high order networks becomes tractable

Lepromatous leprosy patients produce antibodies that recognise non-bilayer lipid arrangements containing mycolic acids

Non-bilayer phospholipid arrangements are three-dimensional structures that form when anionic phospholipids with an intermediate structure of the tubular hexagonal phase II are present in a bilayer of lipids. Antibodies that recognise these arrangements have been described in patients with antiphospholipid syndrome and/or systemic lupus erythematosus and in those with preeclampsia; these antibodies have also been documented in an experimental murine model of lupus, in which they are associated with immunopathology. Here, we demonstrate the presence of antibodies against non-bilayer phospholipid arrangements containing mycolic acids in the sera of lepromatous leprosy (LL) patients, but not those of healthy volunteers. The presence of antibodies that recognise these non-bilayer lipid arrangements may contribute to the hypergammaglobulinaemia observed in LL patients. We also found IgM and IgG anti-cardiolipin antibodies in 77% of the patients. This positive correlation between the anti-mycolic-non-bilayer arrangements and anti-cardiolipin antibodies suggests that both types of antibodies are produced by a common mechanism, as was demonstrated in the experimental murine model of lupus, in which there was a correlation between the anti-non-bilayer phospholipid arrangements and anti-cardiolipin antibodies. Antibodies to non-bilayer lipid arrangements may represent a previously unrecognised pathogenic mechanism in LL and the detection of these antibodies may be a tool for the early diagnosis of LL patients